This is the repository of SQAT, an open-source Sound Quality Analysis Toolbox for MATLAB. It contains a collection of codes implementing key metrics for quantitative sound quality analysis. With SQAT you can conduct quick quantitative sound quality analysis on any calibrated input sound file, in Pascal units. To give a transparent indication of how close the implementations are to the original models, we provide a detailed set of verification routines. Moreover, a number of example codes and exemplary sound files are provided in order to facilitate the initial use of the algorithms.
The toolbox has the following directories:
psychoacoustic_metrics: this directory contains a number of algorithms implementing a specific psychoacoustic metric (see folder).sound_level_meter: contains scripts to obtain sound pressure levels using different frequency weightings (A, B, C, D or Z) and time weightings (fast, slow, or impulse) (see folder).utilities: contains some scripts that are complementary to any of the toolbox functions (see folder).examples: an example script is provided for each metric (see folder).sound_files: this directory hosts reference sounds in .wav format that are used mainly by theexamplescodes (see folder).validation: this directory contains scripts used to validate each algorithm. Instructions on how to run these codes are provided in each respective folder and directly on the header of the codes (see folder).publications: contains scripts to reproduce figures and/or tables of publications from the toolbox authors (see folder).
After downloading this repository, you just need to add the toolbox to the path of your MATLAB. The startup_SQAT code provided can be used to automatically include all folders to the MATLAB path, until the MATLAB session ends. In order to avoid conflicts, the startup_SQAT needs to be used every time MATLAB is (re)started. If you just want to use the metrics, you can add manually only the relevant folders to the MATLAB path (e.g., psychoacoustic_metrics, sound_level_meter, and utilities).
The implemented metrics available in the psychoacoustic_metrics folder are listed in the table below, including the release on which each metric was first introduced:
| Metric | Model | Implementation | Validation | Example | Release |
|---|---|---|---|---|---|
| Loudness | ISO 532-1:2017 [1] | link | link | link | v1.0 |
| Sharpness | DIN 45692:2009 [2] | link | link | link | v1.0 |
| Roughness | Daniel & Weber [3] | link | link | link | v1.0 |
| Fluctuation Strength | Osses et al. [4] | link | link | link | v1.0 |
| Tonality | Aures [5] | link | link | link | v1.0 |
| Psychoacoustic Annoyance | Zwicker & Fastl [6] | link | - | link | v1.0 |
| Psychoacoustic Annoyance | More [7] | link | - | link | v1.0 |
| Psychoacoustic Annoyance | Di et al. [8] | link | - | link | v1.0 |
| EPNL | FAR Part 36 [9] | link | link | link | v1.1 |
The following SPL-based metrics using different frequency weightings (A, B, C, D or Z) and time weightings (fast, slow, or impulse) can be calculated using the codes available in sound_level_meter folder (see examples):
- Sound pressure level over time.
- Equivalent sound pressure level.
- Maximum sound pressure level.
- Sound exposure level.
- Sound spectrum in 1/3 octave bands.
References
[1] International Organization for Standardization. (2017). Acoustics - Methods for calculating loudness - Part 1: Zwicker method (ISO Standard No. 532-1).
[2] Deutsches Institut für Normung. (2009). Measurement technique for the simulation of the auditory sensation of sharpness (DIN Standard No. 45692).
[3] Daniel, P., & Weber, R. (1997). Psychoacoustical Roughness: Implementation of an Optimized Model. Acta Acustica united with Acustica, 83(1), 113-123.
[4] Osses, A., García, R., & Kohlrausch, A. (2016). Modelling the sensation of fluctuation strength. Proceedings of Meetings on Acoustics, 28(1), 050005.
[5] Aures, W. (1985). Berechnungsverfahren für den sensorischen Wohlklang beliebiger Schallsignale (A model for calculating the sensory euphony of various sounds). Acta Acustica united with Acustica, 59(2), 130-141.
[6] Zwicker, E., & Fastl, H. (1999). Psychoacoustics: facts and models, Second edition. Springer-Verlag.
[7] More, S. R. (2010). Aircraft noise characteristics and metrics. PhD thesis, Purdue University.
[8] Di, G. Q., Chen, X. W., Song, K., Zhou, B., & Pei, C. M. (2016). Improvement of Zwicker’s psychoacoustic annoyance model aiming at tonal noises. Applied Acoustics, 105, 164-170.
[9] Federal Aviation Regulations, 14 CFR Parts 36 and 91, Docket No. FAA-2003-16526; Amendment No. 36-26, 91-288, (2005). https://www.ecfr.gov/current/title-14/appendix-Appendix%20A%20to%20Part%2036 (Last viewed 30 Oct 2023)
If you use this toolbox in your research, we would be grateful if you help us to gain visibility by citing SQAT. This is the main citation if you need to cite the toolbox repository itself:
Felix Greco, G., Merino-Martínez, R., & Osses, A. (2023). SQAT: a sound quality analysis toolbox for MATLAB. Zenodo. doi: 10.5281/zenodo.7934709
Tip
The doi above concerns the toolbox repository itself and will always resolve to the latest release. As differences between releases may occur, it is a good practice to cite the specific SQAT version being used. You can check any changes between releases here. If you need to cite a specific release, please consult the relevant doi here. If you need to cite the current SQAT release, please refer to the "Cite this repository" feature in the "About" section of this GitHub repository.
The following paper is the main work describing SQAT and the metrics available in the first release:
Felix Greco, G., Merino-Martínez, R., Osses, A., & Langer, S. C. (2023). SQAT: a MATLAB-based toolbox for quantitative sound quality analysis. INTER-NOISE and NOISE-CON Congress and Conference Proceedings, InterNoise23, Chiba, Japan. doi: 10.3397/IN_2023_1075
Additionally, here's a paper by the members of the SQAT team showing three case studies where the SQAT toolbox was used to perform all analyses:
Osses, A., Felix Greco, G., & Merino-Martínez, R. (2023). Considerations for the perceptual evaluation of steady-state and time-varying sounds using psychoacoustic metrics. Forum Acusticum, Turin, Italy, 11-15 September 2023. doi: 10.61782/fa.2023.0600. Raw data and extra scripts to reproduce all the paper figures can be found here.
We would be very happy to know that you find SQAT useful and have used it in your own work. In this case, please reach out so we can feature your work here.
Vourakis, M., Zea, E., Karlsson, M., Andersson, N., & Etemad, S. (2024). Installation Effects on Axial Fans: Combined Aeroacoustic and Psychoacoustic Perspective. SSRN preprint
Schmidt, H., Yupa-Villanueva, R. M., Ragni, D., Merino-Martinez, R., van Gool, P., & Schmehl, R. (2024). A listening experiment exploring the relationship between noise annoyance and sound quality metrics for airborne energy systems. Wind Energy Science [Preprint]
Kawai, C., Jäggi, J., Georgiou, F., Meister, J., Pieren, R., & Schäffer, B. (2024). Short-term noise annoyance towards drones and other transportation noise sources: A laboratory study. The Journal of the Acoustical Society of America, 156 (4), 2578–2595.
Louwers, G., Pont, S., Gommers, D., van der Heide, E., & Özcan, E. (2024). Sonic ambiances through fundamental needs: An approach on soundscape interventions for intensive care patients, The Journal of the Acoustical Society of America, 156 (4), 2376–2394.
Lotinga, M. J. B., Green, M. C., & Toríja, A. J. (2024). How do flight operations and ambient acoustic environments influence noticeability and noise annoyance associated with unmanned aircraft systems? Quiet Drones 2024 conference
Merino-Martinez, R., Yupa Villanueva, R. M., von den Hoff, B., & Pockelé, J. S. (2024). Human response to the flyover noise of different types of drones recorded in field measurements. Quiet Drones 2024 conference
Georgiou, F., Schäffer, B., Heusser, A., & Pieren, R. (2024). Prediction of Noise Annoyance of Air Vehicle Flyovers Using Psychoacoustic Models. Proceedings of the 30th International Congress on Sound and Vibration (ICSV).
Yupa Villanueva, R. M., Merino-Martinez, R., Andino Cappagli, C. I., Altena, A., & Snellen, M. (2024). Effect of Unmanned Aerial Vehicle Configurations on the Acoustic and Psychoacoustic Signatures. Proceedings of the 30th International Congress on Sound and Vibration (ICSV)
von den Hoff, B., Merino-Martinez, R., Yupa Villanueva, R. M., & Snellen, M. (2024). Noise Emissions and Noise Annoyance of a Single-Propeller Electric Aircraft During Flyover. Proceedings of the 30th International Congress on Sound and Vibration (ICSV)
Pockelé, J. S. & Merino-Martinez, R. (2024). Psychoacoustic Evaluation of Modelled Wind Turbine Noise. Proceedings of the 30th International Congress on Sound and Vibration (ICSV)
Merino-Martinez, R., Ben-Gida, H., & Snellen, M. (2024). Psychoacoustic Evaluation of an Optimized Low-Noise Drone Propeller Design. Proceedings of the 30th International Congress on Sound and Vibration (ICSV)
Yupa Villanueva, R.M., Merino-Martinez, R., Altena, A., & Snellen, M. (2024). Psychoacoustic Characterization of Multirotor Drones in Realistic Flyover Maneuvers. Proceedings of the 30th AIAA/CEAS Aeroacoustics Conference.
Thoma, E.M., Merino-Martinez, R., Grönstedt, T., & Zhao, X. (2024). Noise From Flight Procedure Designed With Statistical Wind: Auralization and Psychoacoustic Evaluation. Proceedings of the 30th AIAA/CEAS Aeroacoustics Conference.
Schade, S., Merino-Martinez, R., Ratei, P., Bartels, S., Jaron, R., & Moreau, A. (2024). Initial Study on the Impact of Speed Fluctuations on the Psychoacoustic Characteristics of a Distributed Propulsion System with Ducted Fans. Proceedings of the 30th AIAA/CEAS Aeroacoustics Conference.
Monteiro, F.d., Merino-Martinez, R., & Lima Pereira, L.T. (2024). Psychoacoustic Evaluation of an Array of Distributed Propellers Under Synchrophasing Operation. Proceedings of the 30th AIAA/CEAS Aeroacoustics Conference.
Merino-Martinez, R., Besnea, I., von den Hoff, B., & Snellen, M. (2024). Psychoacoustic Analysis of the Noise Emissions from the Airbus A320 Aircraft Family and its Nose Landing Gear System. Proceedings of the 30th AIAA/CEAS Aeroacoustics Conference.
Knuth, D., Ring, T. P., & Langer, S. C. (2024). Comparing auralizations and measurements of vibrating plates with physical and psychoacoustic metrics. Proceedings of 50. Jahrestagung für Akustik (DAGA).
Brandetti, L., Mulders, S. P., Merino-Martinez, R., Watson, S., & van Wingerden, J.-W. (2024). Multi-objective calibration of vertical-axis wind turbine controllers: balancing aero-servo-elastic performance and noise. Wind Energy Science, 9, 471-493.
Pockelé, J. S. (2023). Auralisation of modelled wind turbine noise for psychoacoustic listening experiments: development and validation of the wind turbine auralisation tool WinTAur. Master's thesis, Delft University of Technology.
Knuth, D., Ring, T. P., & Langer, S. C. (2023). Utilizing auralization to investigate psychoacoustic perception of vibrating structures. Proceedings of 49. Jahrestagung für Akustik (DAGA).
Motivated by the limited access to well-documented and validated open-source implementations of perceptually-inspired metrics, Gil Felix Greco initiated in the end of 2019 a compilation of algorithms to be used in the evaluation of environmental aircraft noise. In 2021, this set of algorithms was compiled into a MATLAB toolbox, which was named for the first time as a sound quality analysis toolbox (SQAT), and applied to analyze the sound quality of a novel aircraft concept (see publication below). This preliminary SQAT version (not hosted in this repository) required a very specific format of input parameters provided by an aircraft noise prediction research software (PANAM/DLR), suited to the needs of the aforementioned publication.
Felix Greco, G., Bertsch, L., Ring, T. P., & Langer, S. C. (2021). Sound quality assessment of a medium-range aircraft with enhanced fan-noise shielding design. CEAS Aeronautical Journal 12, 481–493.
The actual conception of the SQAT toolbox as published in this repository occurred in October 2022, after inspiring discussions during the ICA conference in South Korea, where Gil met Alejandro and Roberto. Alejandro Osses has been actively contributing to the hearing research community since 2014 and as a developer of the AMT toolbox since 2020 (amtoolbox.org). He provided know-how about code structure and the use of GitHub. Roberto Merino-Martínez has been active in the use and development of sound quality metrics and their actual validation through listening experiments, also contributing some of his code implementations to the team. As a result of Gil's previous efforts and the newly joined forces, we were able to publish the first release of the toolbox, SQAT v1.0 in May 2023. The toolbox is flexible and can be applied to any calibrated sound as directly read from a WAV file.
Within the SQAT team, we are committed to carefully maintaining the toolbox in the long term, thus supporting its use as a reliable and readily accessible tool. The inclusion of new metrics in the future is foreseen as long as enough verification and documentation related to their implementations are (or can be made) available. With this strong requirement, we will only publish implementations that are mature enough for general use, without necessarily requiring further updates after a code is released. We are committed to ensuring backward compatibility of the codes across SQAT releases. However, small unforeseen issues will be fixed as quickly as possible. If that is the case, any changes will be clearly stated it in the documentation. For this reason, we recommend that users always inform which version of SQAT version has been used, and to use the most up-to-date release of the toolbox.
If you would like to get in touch to report a bug, make suggestions or ask a question, please contact us on GitHub by opening an issue.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
